Lifelong Learning Maxent for Suggestion Classification

Authors

  • Thi-Lan Ngo University of Information and Communication Technology, Thainguyen University
  • Tu Vu Hanoi University of Science and Technology
  • Hideaki Takeda National Institute of Informatics, Tokyo
  • Son Bao Pham University of Engineering and Technology, Vietnam National University
  • Xuan Hieu Phan University of Engineering and Technology, Vietnam National University

DOI:

https://doi.org/10.13053/cys-22-4-3107

Keywords:

Suggestion mining, cross-domain suggestion classification, lifelong learning, maximum entropy

Abstract

Suggestion classification for opinion data is defined as identifying a given utterance by suggestion or non-suggestion class. In this paper, we introduce a method called LLMaxent which is the solution for the cross-domain suggestion classification. LLMaxent is a lifelong machine learning approach using maximum entropy (Maxent). In the course of lifelong learning, the drawn knowledge from the past tasks is retained and supported for the future learning. From that, we build a classifier by using labelled data in existed domains for suggestion classification in a new domain. The experimental results show that the proposed novel model can improve the performance of cross-domain suggestion classification. This is one of the preliminary research in lifelong machine learning using Maxent. Its effect is not only for suggestion classification but also for cross-domain text classification in general.

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Published

2018-12-31

Issue

Vol. 22 No. 4 (2018): Topic Trends in Computing Research (Guest Editors: A. Aguilar-Meléndez, E. Moya-Sánchez)

Section

Articles of the Thematic Section

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